Set up and configure an Azure Databricks environment

  • Learning Path
  • 5 Modules

At a glance

Build a solid foundation in Azure Databricks by understanding its architecture, integrations, compute options, and data organization capabilities. Learn how Azure Databricks provides a unified platform for data engineering, analytics, and AI workloads in the cloud.

In this learning path, you'll explore the fundamentals of Azure Databricks and how it fits into the modern data platform ecosystem. You'll start by provisioning workspaces and understanding core workloads, then dive into the architectural concepts that separate control and compute planes. You'll discover how Azure Databricks integrates seamlessly with Microsoft Fabric, Power BI, Visual Studio Code, and other Microsoft services to create comprehensive solutions. You'll learn to select and configure the right compute resources for your workloads, optimizing for both performance and cost. Finally, you'll master Unity Catalog's organizational structure to effectively manage your data assets. By the end, you'll have the foundational knowledge needed to build scalable data solutions on Azure Databricks.

Prerequisites

  • Fundamental knowledge of data analytics concepts
  • Basic understanding of cloud storage concepts
  • Familiarity with SQL and data organization principles

Modules in this learning path

Explore Azure Databricks

Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.

Understand Azure Databricks architecture

Azure Databricks architecture separates control and compute planes while organizing resources through a hierarchical structure. This module explores how the account hierarchy works, the differences between serverless and classic compute planes, and the various storage options available including default storage, external storage, and Unity Catalog managed storage for organizing and governing your data.

Understand Azure Databricks Integrations

Azure Databricks integrates with multiple Microsoft services to provide end-to-end data engineering, analytics, and AI capabilities. This module explores how Azure Databricks works with Microsoft Fabric, Power BI, Visual Studio Code, Power Platform, Copilot Studio, Microsoft Purview, and Microsoft Foundry to enable comprehensive solutions that combine data lakehouse capabilities with business intelligence, application development, and conversational AI.

Select and Configure Compute in Azure Databricks

Azure Databricks provides multiple compute options optimized for different workloads. This module explores how to choose the right compute type, configure performance settings, manage access permissions, and install libraries. You'll learn when to use serverless versus classic compute, how to optimize clusters for cost and performance, and best practices for securing compute resources.

Create and organize objects in Unity Catalog

Unity Catalog's three-layer namespace—catalogs, schemas, and objects—provides a flexible foundation for organizing data assets while maintaining centralized governance. This module explores how to create catalogs for environment isolation, organize schemas within those catalogs, and create tables, views, and volumes for structured and unstructured data. You'll learn to implement foreign catalogs for external database access, apply effective naming conventions, and configure AI/BI Genie instructions to enhance data discoverability.

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